Fully Automatic Segmentation of Brain Tumor Images Using Support Vector Machine Classification in Combination with Hierarchical Conditional Random Field Regularization

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6893 LNCS, Issue PART 3, 2011, Pages 354-361

  Bauer, Stefan ^a   Nolte, Lutz P ^a   Reyes, Mauricio ^a  

^a UNIVERSITY OF BERN   (Switzerland)

Author keywords

Brain Tumor; CRF; Glioma; MRI; Segmentation; SVM

Indexed keywords

ACQUISITION PROTOCOLS; AUTOMATIC METHOD; AUTOMATIC SEGMENTATIONS; BRAIN CANCER; BRAIN TISSUE; BRAIN TUMORS; CEREBRO SPINAL FLUIDS; COMPUTATION TIME; CONDITIONAL RANDOM FIELD; CRF; GLIOMA; GRAY MATTER; HIERARCHICAL APPROACH; MAGNETIC RESONANCE IMAGES; MULTI-SPECTRAL; PATIENT DATASETS; SPATIAL CONSTRAINTS; SUPPORT VECTOR MACHINE CLASSIFICATION; SVM; SVM CLASSIFICATION; TUMOR TISSUES; WHITE MATTER;

BRAIN; COMPUTER AIDED ANALYSIS; MAGNETIC RESONANCE; MAGNETIC RESONANCE IMAGING; MEDICAL COMPUTING; MEDICAL IMAGING; SUPPORT VECTOR MACHINES; TUMORS;

IMAGE SEGMENTATION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BRAIN TUMOR; DIAGNOSTIC IMAGING; GLIOMA; HUMAN; IMAGE PROCESSING; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING; PATHOLOGY; PROBABILITY; REPRODUCIBILITY; STATISTICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BRAIN NEOPLASMS; DIAGNOSTIC IMAGING; GLIOMA; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MARKOV CHAINS; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS;

EID: 82255181699     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-23626-6_44     Document Type: Conference Paper

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